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Robust Vision Technology of Intelligent Systems for Real-world Applications

Lessons learned from failures are that“robustness” of computer vision is important. Firstly, robust against “illumination changes”. Camera parameters, “ISO gain, aperture (=F#), exposure” determine the image quality. It is designed mainly for Photography (not for Robot),correlated non-linearly and sensitive to illumination changes.So it needs a very simple, but effective way to control the camera parameters for “Robots”. Secondly, robust against “outliers”. A novel robust PCA model for outliers is necessary due to bad weather. According to the real-time“see-through” car system, cars are equipped with many cameras and sensors, vehicle to vehicle (V2V) communication are built for autonomous and assisted driving, making other cars transparent using cameras and sensors via wireless network. Thirdly, robust against “complex environments”. By deep learning model, the system has the object recognition function. It can detect object(DET), and make classification and localization (CLS-LOC). Lastly, robust against “difficult conditions”. Sensor fusion approach is important for high-quality 3D modeling. In a word, robustness of computer vision solutions is a very important key for the real-world applications of intelligent systems (automobile, robots), and it involves camera input enhancement ,real-time outlier handling, deep-learning, sensor fusion and many other issues.

Kweon In-So(權(quán)仁昭),卡內(nèi)基梅隆大學(xué)機器人研究所博士, 韓國科學(xué)技術(shù)院電氣工程教授(EE),韓國國家關(guān)鍵技術(shù)研究中心-KAIST P3數(shù)字車中心主任。1995—1998年擔任韓國科學(xué)技術(shù)院自動化工程及設(shè)計部門主管(ADE)。Kweon教授主要從事計算機視覺和機器人研究，為KROS、ICROS和IEEE會員，自2005年為《國際計算機視覺雜志》的編輯委員會成員。Kweon教授在國際會議上曾獲多個獎項，包括“IEEE-CVPR2009最佳學(xué)生論文獎的亞軍”以及“ICCAS2008學(xué)生論文獎”。2001年，Kweon教授獲得了KAIST研究獎。

Kweon In-So

(School of Electrical Engineering, KAIST, Korea)